Generating comprehensive scene graphs with integrated multiple attribute detection

Scene graphs present the semantic highlights of the underlying image in directed graph form. Their automated generation is often restricted to detecting multiple relations for objects. The diverse attributes of the objects are neglected in the process. We propose a Multiple Attribute Detector that c...

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Veröffentlicht in:Machine vision and applications 2023, Vol.34 (1), p.11, Article 11
Hauptverfasser: Patil, Charulata, Abhyankar, Aditya
Format: Artikel
Sprache:eng
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Zusammenfassung:Scene graphs present the semantic highlights of the underlying image in directed graph form. Their automated generation is often restricted to detecting multiple relations for objects. The diverse attributes of the objects are neglected in the process. We propose a Multiple Attribute Detector that can capture structured attribute information of an object, i.e. attribute type, value in the form of a triplet. The module is capable of generating multiple such triplets for every detected object in the scene. It can be integrated with existing scene graph generation frameworks without altering relation detection mechanism to yield comprehensive scene graphs. We have also created new datasets for this purpose that include attribute type-value data per object.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-022-01361-3